Faculty Publications

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Publications by NITK Faculty

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    DoA Estimation for Micro and Nano UAV Targets using AWR2243 Cascaded Imaging Radar
    (Institute of Electrical and Electronics Engineers Inc., 2022) Kavya, T.S.; Vandana, G.S.; Srihari, P.; Leelarani, V.; Pardhasaradhi, B.
    Frequency-modulated continuous wave (FMCW) radars accurately estimate the target's position and velocity, but the angular resolution is inadequate. The low radar cross section (RCS) unmanned aerial vehicles (UAVs) like micro UAVs (0.01m2) and nano UAVs (0.001m2) pose a significant threat to sensitive military and civilian installations. The DoA of the low RCS targets helps in making stealthy countermeasures. In this paper, the DoA of nano and micro UAVs is experimented using Texas instruments AWR2243 cascaded imaging radar in conjunction with a digital signal processing evaluation module (DSP EVM). The data is received from all the available 16 receivers, then the subspace method of multiple signal classification (MUSIC) algorithm is applied to estimate the DoA of the low RCS UAvs in hovering mode. The ground truth of the UAVs is fixed at 10m range and 12 ° azimuth from the center of the radar using engineering protractor. The average estimated DoA for nano and micro UAV s is 12.80° and 11.43°, respectively, for the ground truth DoA. The AWR2243 cascaded imaging radar provides superior performance and suitable candidate for the DoA estimation for micro and nano UAVs compared to existing AWR1642, IWR1642, and IWR6843 radars. © 2022 IEEE.
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    Efficient Coherent Direction-of-Arrival Estimation and Realization Using Digital Signal Processor
    (Institute of Electrical and Electronics Engineers Inc., 2020) Dakulagi, V.; Alagirisamy, M.; Singh, M.
    A novel efficient coherent direction-of-arrival (DOA) estimation method is devised in this article. First, a new cost function without the knowledge of source number is developed exploiting the Toeplitz matrices' joint diagonalization structure. Then, the revised steering vectors are used in the place of projection weights of the steering vectors to reconstruct the power spectrum in both noise and signal subspaces. The coherent DOAs are estimated using the 1-D search. Furthermore, the computational complexity of the proposed method is significantly reduced using the Nystrom approximation. Finally, the developed theoretical model is implemented on the TMS320C6678 digital signal processor (DSP) to exemplify the efficacy of the novel method. © 1963-2012 IEEE.
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    Bit error rate analysis of ground-to-high altitude platform free-space optical communications using coded polarization shift keying in various weather conditions
    (Springer, 2022) Nallagonda, V.; Krishnan, P.
    High altitude platforms (HAPs) aided free-space optical (FSO) communication, a future emerging technology for next-generation communication systems. HAP aided FSO communication systems, contributing significantly to data hunger applications. Weather conditions, angle of arrival fluctuations, blockages, and pointing error loss due to the HAP’s hovering state are some of the limitations to establishing an efficient link. In this paper, we proposed for the first time a Ground-to-HAP FSO communication system based on polarization shift keying to improve performance under hovering fluctuations. We also improved the proposed system’s performance by employing BCH and repetition coding schemes. The proposed system’s average bit error rate performance is expressed in closed form, and the results are analysed under various weather conditions such as rain (light and heavy) and fog (light and moderate). The results for coded and uncoded cases are compared. The achieved coding gain is 28.5 dB. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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    Polyphonic sound event localization and detection using channel-wise FusionNet
    (Springer, 2024) V, S.; Kooolagudi, S.G.
    Sound Event Localization and Detection (SELD) is the task of spatial and temporal localization of various sound events and their classification. Commonly, multitask models are used to perform SELD. In this work, a deep learning network model named channel-wise ‘FusionNet’ is designed to perform the SELD task. The novel fusion layer is introduced into the regular Deep Neural Network (DNN), where the input is fed channel-wise, and the outputs of all channels are fused to form a new feature representation. The key contribution of this work is the neural network model which helps to retain the channel-wise information from the multichannel input along with the spatial and temporal information. The proposed network utilizes separable convolution blocks in the convolution layers, therefore, the complexity of the model is low in terms of both time and space. The features used as input are Mel-band energies for Sound Event Detection (SED) and intensity vectors for the Direction-of-Arrival (DOA) estimation. The proposed network’s fusion layer provides a better representation of features for both SED and DOA estimation tasks. Experiments are performed on the recordings of the First-order Ambisonic (FOA) array format of the TAU-NIGENS Spatial Sound Events 2020 dataset. An improved performance is achieved in terms of Error Rate (ER), DOA error, and Frame Recall (FR) has been observed in comparison to the state-of-the-art SELD systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
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    CUSE-TD Algorithm for Precision Detection of Coherent and Uncorrelated Signals
    (Institute of Electrical and Electronics Engineers Inc., 2025) Dakulagi, R.; Raushan, R.; Dutta, S.; Theeb Alosaimi, M.; Villagomez-Galindo, M.
    This paper presents a novel algorithm, Coherent and Uncorrelated Sources Estimation using Tensor Decomposition (CUSE-TD), designed to enhance the accuracy of Direction of Arrival (DOA) estimation in Multiple-Input Multiple-Output (MIMO) systems. The CUSE-TD algorithm addresses the complex challenge of estimating both coherent and uncorrelated sources by leveraging advanced sensor-driven tensor decomposition techniques, with an emphasis on Tucker decomposition. By meticulously analyzing factor matrices derived from sensor data, the algorithm effectively decodes intricate spatiotemporal patterns within the received tensor, offering precise source estimation even in scenarios where the number and nature of the sources are unknown. The paper provides an in-depth assessment of the algorithm's performance, highlighting its computational efficiency and ability to handle diverse sensor-driven scenarios. Extensive simulations in intricate sensing environments demonstrate the algorithm's robustness, affirming its potential to significantly improve DOA estimation accuracy in MIMO systems across a wide range of operational conditions. The usefulness of the CUSE-TD algorithm lies in its applicability CUSE -TD to real-world MIMO systems, particularly in complex environments such as radar, wireless communications, and surveillance systems, where accurate source detection is critical for operational effectiveness. Its adaptability to varying sensor configurations and source conditions makes it a powerful tool for enhancing overall system reliability and performance. © 2013 IEEE.